2013/09/01 by Federica Giummolè, Salvatore Orlando, Gabriele Tolomei · 1 citation
Medicine · Physics and Astronomy · Social Sciences · #Data-Driven Disease Surveillance #Complex Network Analysis Techniques #Human Mobility and Location-Based Analysis
paper · doi:10.1109/socialcom.2013.12
openalex publication_date 2013/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Once every five minutes, Twitter publishes a list of trending topics by monitoring and analyzing tweets from its users. Similarly, Google makes available hourly a list of hot queries that have been issued to the search engine. In this work, we analyze the time series derived from the daily volume index of each trend, either by Twitter or Google. Our study on a real-world dataset reveals that about 26% of the trending topics raising from Twitter "as-is" are also found as hot queries issued to Google. Also, we find that about 72% of the similar trends appear first on Twitter. Thus, we assess the relation between comparable Twitter and Google trends by testing three classes of time series regression models. We validate the forecasting power of Twitter by showing that models, which use Google as the dependent variable and Twitter as the explanatory variable, retain as significant the past values of Twitter 60% of times.